We conduct research on integrating knowledge representation and reasoning techniques with robotics. Our goal is to endow robots with sophisticated, human-like behavior. Our work includes the design and implementation of logic formalisms, representation methodologies, and logic-based reasoning algorithms. We focus on domains that are knowledge intensive and require sophisticated reasoning capabilities. Ultimately, we aim to define agent architectures capable of planning, diagnostics, and execution monitoring in the kinds of domains a realistic robot might encounter and under the computational constraints a realistic robotic architecture would impose. Our projects include:
- Languages, solvers and reasoning mechanisms for hybrid qualitative-quantitative knowledge
- Planning, diagnostics, learning in discrete and hybrid discrete-continuous domains
- Agent architectures
We believe that sophistication in Natural Language Understanding can only be achieved if agents are endowed with the ability to understand how the world described by natural language passages evolves as the story progresses. Doing so requires being able to reason about the direct and indirect effects of actions, about the plausible causes of observations, about goals and intentions of agents, and to use such reasoning processes to identify ways of filling the gaps in a story.
Our work in cybersecurity is aimed at increasing the breadth of understanding of threats and of mitigation strategies by integrating cybersecurity, knowledge representation and automated reasoning. Our projects include: